Complexity-Based Analysis of Heart Rate Variability During Aging

Analysis of brain activity for patients with brain disorders is an important research area. Mild cognitive impairment (MCI) is a condition in which patients have more memory or thinking problems compared to healthy people of the same age. In this work, we studied the alterations in brain activity among control subjects and patients with MCI. Three complexity techniques, namely sample entropy, approximate entropy, and fractal dimension, were employed to study electroencephalogram (EEG) signals recorded from 102 control (healthy) subjects, and seven subjects with MCI in a comfortable position, on a bed, with their eyes closed. The results showed that the EEG signals of patients with MCI show greater complexity than the EEG signals of healthy subjects. This analysis method can be applied to compare brain activity among healthy subjects and patients with other brain diseases.

Investigating the Variations in the Brain Activity Between Healthy Subjects and Mild Cognitive Impairment (Mci) Patients

PAKNIYAT,

RAMAKRISHNAN,

PALLAVI

et al. 2023

Analysis of the Changes in the Brain Activity Between Rest and Multitasking Workload by Complexity-Based Analysis of Eeg Signals

PARTHASARATHY,

MARESOVA,

RAJAGOPAL

et al. 2023

Analysis of the changes in brain activity between rest and various conditions is an important area of research. We investigated the changes in brain activity among rest and multitask workload (SIMKAP multitasking test) by quantifying the complexity of EEG signals. The results showed that EEG signals have smaller fractal dimensions, sample entropy, and approximate entropy during the SIMKAP multitasking test than the rest. Therefore, the complexity of EEG signals was lower during the SIMKAP multitasking test than the rest. In further research, we can study the changes in brain activity among other conditions, which has great benefits in decoding brain activity in various conditions.

Complexity-Based Analysis in Biomedical Image Analysis: A Review

PAKNIYAT,

ABDULLAH,

KREJCAR

et al. 2024

This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such as fractal dimension, entropy measures, and network analysis in characterizing the complexity of biomedical images (e.g. magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray images). Additionally, we discuss the clinical implications of complexity-based analysis in areas such as cancer detection, neuroimaging, and cardiovascular imaging, highlighting its potential to improve diagnostic accuracy, prognostic assessment, and treatment outcomes. The review concludes that complexity-based analysis significantly enhances the interpretability and diagnostic power of biomedical imaging, paving the way for more personalized and precise medical care. By elucidating the role of complexity-based analysis in biomedical image analysis, this review aims to provide insights into current trends, challenges, and future directions in this rapidly evolving field.